Recognizing targets in images is a challenge to existing detection systems. One of the primary functions of automatic target recognition is to find candidate targets and separate them from clutter. This process can be defined as target detection. Using image data for automatic target detection, a sample of target pixels can be statistically different from a sample of background pixels in the immediate neighborhood of the candidate target. Algorithms then can be devised to recognize groups or individual outlying pixels as indicating a possible target to be further processed by an automatic target recognition algorithm. Conventional detection systems can analyze images for pixel distortion to indicate a difference from the background or clutter, this difference being a candidate target.
Received images can be filtered and processed using conventional methods to detect peaks of energy that identify candidate targets. Problems, however, occur when the peaks of energy are not so readily identifiable compared with the background, or clutter. Further, false alarms occur when clutter or other non-target image data is tagged as a candidate target. Processing detection resources are wasted in these efforts. Thus, in conventional systems, potential candidate targets cannot be readily identified, or resources are used inefficiently.